Systems and methods for predicting pest pressure using geospatial features and machine learning

ABSTRACT

System and methods for predicting future pest pressures are provided. A pest pressure prediction computing device includes a memory and a processor communicatively coupled to the memory. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, receive weather data for the geographic location, receive image data for the geographic location, identify at least one geospatial feature within or proximate to the geographic location, apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature, and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to provisional application Ser. No.62/984,885, filed Mar. 4, 2020, which is incorporated herein byreference in its entirety.

BACKGROUND

The present application relates generally to a technology that may beused to assist in predicting pest pressure, and more particularly, tonetwork-based systems and methods for predicting pest pressure usinggeospatial features and machine learning.

Due to the world's increasing population and decreasing amount of arableland, there is a desire for methods and systems to increase theproductivity of agricultural crops. At least one factor that impacts theproductivity of agricultural crops is pest pressure.

Accordingly, systems and methods have been developed to monitor andanalyze pest pressure. For example, in at least some known systems, aplurality of insect traps are placed in a field of interest. To monitorthe pest pressure in the field of interest, the traps are inspectedregularly to count the number of pests in each trap. Based on the numberof pests in each trap, a pest pressure level for the field of interestcan be determined.

The number of pests monitored in each trap may also be used to predictfuture pest pressures. However, pest pressure is a relatively complexphenomenon that is governed by several factors. Thus, accuratelypredicting future pest pressures based primarily on trap counts may berelatively inaccurate. Further, at least some known systems for pestpressure monitoring are focused at an individual farm level, resultingin limited visualizations and significant time lag in data collections.In addition, at least some known systems for predicting future pestpressure rely on static logic (e.g., fixed phenology models and/ordecision trees), and are accordingly limited in their ability toaccurately predict future pest pressure.

Accordingly, it would be desirable to provide a system that captures andintelligently analyzes a plurality of different types of information toquickly and accurately predict future pest pressures. Further, it wouldbe desirable to present predicted future pest pressures to assist usersin performing the technical task of monitoring pest pressure, andoptionally controlling a pest trap system and/or a pest treatmentsystem.

BRIEF DESCRIPTION

In one aspect, a pest pressure prediction computing device is provided.The pest pressure prediction computing device includes a memory, and aprocessor communicatively coupled to the memory. The processor isprogrammed to receive trap data for a plurality of pest traps in ageographic location, the trap data including at least current andhistorical pest pressure at each of the plurality of pest traps, receiveweather data for the geographic location, the weather data including atleast current and historical weather conditions for the geographiclocation, receive image data for the geographic location, identify atleast one geospatial feature within or proximate to the geographiclocation, apply a machine learning algorithm to the trap data, theweather data, the image data, and the at least one identified geospatialfeature to identify a correlation between pest pressure and the at leastone geospatial feature, and generate predicted future pest pressures forthe geographic location based at least on the identified correlationbetween pest pressure and the at least one geospatial feature.

In another aspect, a method for generating pest pressure predicationdata is provided. The method is implemented using a pest pressureprediction computing device including a memory communicatively coupledto a processor. The method includes receiving trap data for a pluralityof pest traps in a geographic location, the trap data including at leastcurrent and historical pest pressure at each of the plurality of pesttraps, receiving weather data for the geographic location, the weatherdata including at least current and historical weather conditions forthe geographic location, receiving image data for the geographiclocation, identifying at least one geospatial feature within orproximate to the geographic location, applying a machine learningalgorithm to the trap data, the weather data, the image data, and the atleast one identified geospatial feature to identify a correlationbetween pest pressure and the at least one geospatial feature, andgenerating predicted future pest pressures for the geographic locationbased at least on the identified correlation between pest pressure andthe at least one geospatial feature.

In yet another aspect, a computer-readable storage medium havingcomputer-executable instructions embodied thereon is provided. Whenexecuted by a pest pressure prediction computing device including atleast one processor in communication with a memory, thecomputer-readable instructions cause the pest pressure predictioncomputing device to receive trap data for a plurality of pest traps in ageographic location, the trap data including at least current andhistorical pest pressure at each of the plurality of pest traps, receiveweather data for the geographic location, the weather data including atleast current and historical weather conditions for the geographiclocation, receive image data for the geographic location, identify atleast one geospatial feature within or proximate to the geographiclocation, apply a machine learning algorithm to the trap data, theweather data, the image data, and the at least one identified geospatialfeature to identify a correlation between pest pressure and the at leastone geospatial feature, and generate predicted future pest pressures forthe geographic location based at least on the identified correlationbetween pest pressure and the at least one geospatial feature.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1-10 show example embodiments of the methods and systems describedherein.

FIG. 1 is a block diagram of a computer system used in predicting pestpressures in accordance with one embodiment of the present disclosure.

FIG. 2 is a block diagram illustrating data flow through the systemshown in FIG. 1.

FIG. 3 illustrates an example configuration of a server system such asthe pest pressure prediction computing device of FIGS. 1 and 2.

FIG. 4 illustrates an example configuration of a client system shown inFIGS. 1 and 2.

FIG. 5 is a flow diagram of an example method for generating pestpressure data using the system shown in FIG. 1.

FIG. 6 is a flow diagram of an example method for generating heat mapsusing the system shown in FIG. 1.

FIGS. 7-10 are screenshots of a user interface that may be generatedusing the system shown in FIG. 1

Although specific features of various embodiments may be shown in somedrawings and not in others, this is for convenience only. Any feature ofany drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

DETAILED DESCRIPTION

The systems and methods described herein are directed tocomputer-implemented systems for predicting future pest pressures usingmachine learning. A pest pressure prediction computing device includes amemory and a processor communicatively coupled to the memory. Theprocessor is programmed to receive trap data for a plurality of pesttraps in a geographic location, the trap data including at least currentand historical pest pressure at each of the plurality of pest traps. Theprocessor is further programmed to receive weather data for thegeographic location, the weather data including at least current andhistorical weather conditions for the geographic location, and receiveimage data for the geographic location. Further, the processor isprogrammed to identify at least one geospatial feature within orproximate to the geographic location, and apply a machine learningalgorithm to the trap data, the weather data, the image data, and the atleast one identified geospatial feature to identify a correlationbetween pest pressure and the at least one geospatial feature. Based onthe at least one identified correlation, the processor is programmed togenerate predicted future pest pressures for the geographic location.

The systems and methods described herein facilitate accuratelypredicting pest pressure at one or more geographic locations. As usedherein, a ‘geographic location’ generally refers to an agriculturallyrelevant geographic location (e.g., a location including one or morefields and/or farms for producing crops). Further, as used here, ‘pestpressure’ refers to a qualitative and/or quantitative assessment of theabundance of pests present at a particular location. For example, a highpest pressure indicates that a relatively large abundance (e.g., ascompared to an expected abundance) of pests are present at the location.In contrast, a low pest pressure indicates that a relatively lowabundance of pests are present at the location. In at least some of theembodiments described herein, pest pressure is analyzed for agriculturalpurposes. That is, pest pressure is monitored and predicted for one ormore fields. However, those of skill in the art will appreciate that thesystems and methods described herein may be used to analyze pestpressure in any suitable environment.

As used herein, the term ‘pest’ refers to an organism whose presence isgenerally undesirable at the particular geographic location, inparticular an agriculturally relevant geographic location. For example,for implementations that analyze pest pressure for one or more fields,pests may include insects that have a propensity to damage crops inthose fields. However, those of skill in the art will appreciate thatthe systems and methods described herein may be used to analyze pestpressure for other types of pests. For example, in some embodiments,pest pressure may be analyzed for fungi, weeds, and/or diseases. Thesystems and methods described herein refer to ‘pest traps’ and ‘trapdata’. As used herein, ‘pest traps’ may refer to any device capable ofcontaining and/or monitoring presence of a pest of interest, and ‘trapdata’ may refer to data gathered using such a device. For example, forinsects, the ‘pest trap’ may be a conventional containment device thatsecures the pest. Alternatively, for fungi, weeds, or diseases, the‘pest trap’ may refer to any device capable of monitoring presenceand/or levels of the fungi, weeds, and/or diseases. For example, inembodiments where the ‘pest’ is one or more species of fungi, the ‘pesttrap’ may refer to a sensing device capable of quantitatively measuringa level of spores associated with the one or more species of fungi inthe ambient environment around the sensing device. In one embodiment,the ‘pest’ is a type of insect or multiple types of insects, and theterms ‘pest trap’ and ‘pest traps’ refer to ‘insect trap’ and ‘insecttraps’, respectively.

The following detailed description of the embodiments of the disclosurerefers to the accompanying drawings. The same reference numbers indifferent drawings may identify the same or similar elements. Also, thefollowing detailed description does not limit the claims.

Described herein are computer systems such as pest pressure predictioncomputing devices. As described herein, all such computer systemsinclude a processor and a memory. However, any processor in a computerdevice referred to herein may also refer to one or more processorswherein the processor may be in one computing device or a plurality ofcomputing devices acting in parallel. Additionally, any memory in acomputer device referred to herein may also refer to one or morememories wherein the memories may be in one computing device or aplurality of computing devices acting in parallel.

As used herein, a processor may include any programmable systemincluding systems using micro-controllers, reduced instruction setcircuits (RISC), application specific integrated circuits (ASICs), logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are example only, and arethus not intended to limit in any way the definition and/or meaning ofthe term “processor.”

As used herein, the term “database” may refer to either a body of data,a relational database management system (RDBMS), or to both. As usedherein, a database may include any collection of data includinghierarchical databases, relational databases, flat file databases,object-relational databases, object-oriented databases, and any otherstructured collection of records or data that is stored in a computersystem. The above examples are example only, and thus are not intendedto limit in any way the definition and/or meaning of the term database.Examples of RDBMS's include, but are not limited to including, Oracle®Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, andPostgreSQL. However, any database may be used that enables the systemsand methods described herein. (Oracle is a registered trademark ofOracle Corporation, Redwood Shores, Calif.; IBM is a registeredtrademark of International Business Machines Corporation, Armonk, N.Y.;Microsoft is a registered trademark of Microsoft Corporation, Redmond,Wash.; and Sybase is a registered trademark of Sybase, Dublin, Calif.)

In one embodiment, a computer program is provided, and the program isembodied on a computer readable medium. In an example embodiment, thesystem is executed on a single computer system, without requiring aconnection to a sever computer. In a further embodiment, the system isbeing run in a Windows® environment (Windows is a registered trademarkof Microsoft Corporation, Redmond, Wash.). In yet another embodiment,the system is run on a mainframe environment and a UNIX® serverenvironment (UNIX is a registered trademark of X/Open Company Limitedlocated in Reading, Berkshire, United Kingdom). The application isflexible and designed to run in various different environments withoutcompromising any major functionality. In some embodiments, the systemincludes multiple components distributed among a plurality of computingdevices. One or more components may be in the form ofcomputer-executable instructions embodied in a computer-readable medium.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralelements or steps, unless such exclusion is explicitly recited.Furthermore, references to “example embodiment” or “one embodiment” ofthe present disclosure are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by aprocessor, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexample only, and are thus not limiting as to the types of memory usablefor storage of a computer program.

The systems and processes are not limited to the specific embodimentsdescribed herein. In addition, components of each system and eachprocess can be practiced independent and separate from other componentsand processes described herein. Each component and process also can beused in combination with other assembly packages and processes.

The following detailed description illustrates embodiments of thedisclosure by way of example and not by way of limitation. It iscontemplated that the disclosure has general application to predictingpest pressure.

FIG. 1 is a block diagram of an example embodiment of a computer system100 used in predicting pest pressures that includes a pest pressureprediction (PPP) computing device 112 in accordance with one exampleembodiment of the present disclosure. PPP computing device 112 may alsobe referred to herein as a heat map generation computing device, asdescribed herein. In the example embodiment, system 100 is used forpredicting pest pressures and generating pest pressure heat maps, asdescribed herein.

More specifically, in the example embodiment, system 100 includes pestpressure prediction (PPP) computing device 112, and a plurality ofclient sub-systems, also referred to as client systems 114, connected toPPP computing device 112. In one embodiment, client systems 114 arecomputers including a web browser, such that PPP computing device 112 isaccessible to client systems 114 using the Internet and/or using network115. Client systems 114 are interconnected to the Internet through manyinterfaces including a network 115, such as a local area network (LAN)or a wide area network (WAN), dial-in-connections, cable modems, specialhigh-speed Integrated Services Digital Network (ISDN) lines, and RDTnetworks. Client systems 114 may include systems associated withfarmers, growers, scouts, etc. as well as external systems used to storedata. PPP computing device 112 is also in communication with one or moredata sources 130 using network 115. Further, client systems 114 mayadditionally communicate with data sources 130 using network 115.Further, in some embodiments, one or more client systems 114 may serveas data sources 130, as described herein. Client systems 114 may be anydevice capable of interconnecting to the Internet including a web-basedphone, PDA, or other web-based connectable equipment.

A database server 116 is connected to a database 120, which containsinformation on a variety of matters, as described below in greaterdetail. In one embodiment, centralized database 120 is stored on PPPdevice 112 and can be accessed by potential users at one of clientsystems 114 by logging onto PPP computing device 112 through one ofclient systems 114. In an alternative embodiment, database 120 is storedremotely from PPP device 112 and may be non-centralized. Database 120may be a database configured to store information used by PPP computingdevice 112 including, for example, transaction records, as describedherein.

Database 120 may include a single database having separated sections orpartitions, or may include multiple databases, each being separate fromeach other. Database 120 may store data received from data sources 130and generated by PPP computing device 112. For example, database 120 maystore weather data, imaging data, trap data, scouting data, grower data,pest pressure prediction data, and/or heat map data, as described indetail herein.

In the example embodiment, client systems 114 may be associated with,for example, a grower, a scouting entity, a pest management entity,and/or any other party capable of using system 100 as described herein.In the example embodiment, at least one of client systems 114 includes auser interface 118. For example, user interface 118 may include agraphical user interface with interactive functionality, such that pestpressure predictions and/or heat maps, transmitted from PPP computingdevice 112 to client system 114, may be shown in a graphical format. Auser of client system 114 may interact with user interface 118 to view,explore, and otherwise interact with the displayed information.

In the example embodiment, PPP computing device 112 receives data from aplurality of data sources 130, and aggregates and analyzes the receiveddata (e.g., using machine learning) to generate pest pressurepredictions and/or heat maps, as described in detail herein.

FIG. 2 is a block diagram illustrating data flow through system 100. Inthe embodiment shown in FIG. 2, data sources 130 include a weather datasource 202, an imaging data source 204, a trap data source 206, ascouting data source, a grower data source 210, and an another datasource 212. Those of skill in the art will appreciate that data sources130 shown in FIG. 2 are merely examples, and that system 100 may includeany suitable number and type of data source.

Weather data source 202 provides weather data to PPP computing device112 for use in generating pest pressure predictions. Weather data mayinclude, for example, temperature data (e.g., indicating current and/orpast temperatures measured at one or more geographic locations),humidity data (e.g., indicating current and/or past humidity at measuredat one or more geographic locations), wind data (e.g., indicatingcurrent and/or past wind levels and direction measured at one or moregeographic locations), rainfall data (e.g., indicating current and/orpast rainfall levels measured at one or more geographic locations), andforecast data (e.g., indicating future weather conditions predicted forone or more geographic locations).

Imaging data source 204 provides image data to PPP computing device 112for use in generating pest pressure predictions. Image data may include,for example, satellite images and/or drone images acquired of one ormore geographic locations.

Trap data source 206 provides trap data to PPP computing device 112 foruse in generating pest pressure predictions. Trap data may include, forexample, pest counts (e.g., expressed as number of a pest species,density of the pest species, or the like) from at least one pest trap ina geographic location. Further, trap data may include, for example, inthe case of insects, pest type (e.g., taxonomic genus, species, variety,etc.) and/or pest developmental stage and gender (e.g., larva, juvenile,adult, male, female, etc.). The pest traps may be, for example, insecttraps. Alternatively, the pest traps may be any device capable ofdetermining a pest presence and providing trap data to PPP computingdevice 112 as described herein. For example, in some embodiments, thepest traps are sensing devices operable to sense an ambient level ofspores associated with one or more species of fungi. In suchembodiments, the trap data may include, for example, number of spores(representing the pest count), fungus type, fungus developmental stage,etc.

In some embodiments, trap data source 206 is a pest trap that iscommunicatively coupled to PPP computing device 112 (e.g., over awireless communication link). Accordingly, in such embodiments, trapdata source 206 may be capable of automatically determining a pest countin the pest trap (e.g., using image processing algorithms) andtransmitting the determined pest count to PPP computing device.

Scouting data source 208 provides scouting data to PPP computing device112 for use in generating pest pressure predictions. Scouting data mayinclude any data provided by a human scout that monitors one or moregeographic locations. For example, the scouting data may include cropcondition, pest counts (e.g., manually counted at a pest trap by thehuman scout), etc. In some embodiments, scouting data source 208 is oneof client systems 114. That is, a scout can both provide scouting datato PPP computing device 112 and view pest pressure prediction dataand/or heat map data using the same computing device (e.g., a mobilecomputing device).

Grower data source 210 provides grower data to PPP computing device 112for use in generating pest pressure predictions. Grower data mayinclude, for example, field boundary data, crop condition data, etc.Further, similar to scouting data source 208, in some embodiments,grower data source 210 is one of client systems 115. That is, a growercan both provide scouting data to PPP computing device 112 and view pestpressure prediction data and/or heat map data using the same computingdevice (e.g., a mobile computing device).

Other data source 212 may provide other types of data to PPP computingdevice 112 that are not available from data sources 202-210. Forexample, in some embodiments, other data source 212 includes a mappingdatabase that provides mapping data (e.g., topographical maps of one ormore geographic locations) to PPP computing device 112.

In the example embodiment, PPP computing device 112 receives data fromat least one of data sources 202-212, and aggregates and analyzes thatdata (e.g., using machine learning) to generate pest pressure predictiondata, as described herein. Further, PPP computing device 112 may alsoaggregate and analyze that data to generate heat map data, as describedherein. The pest pressure prediction data and/or heat map data may betransmitted to client system 114 (e.g., for displaying to a user ofclient system 114).

In some embodiments, data from at least one of data sources 202-210 isautomatically pushed to PPP computing device 112 (e.g., without PPPcomputing device 112 polling or querying data sources 202-210). Further,in some embodiments, PPP computing device 112 polls or queries (e.g.,periodically or continuously) at least one of data sources 202-210 toretrieve the associated data.

FIG. 3 illustrates an example configuration of a server system 301 suchas PPP computing device 112 (shown in FIGS. 1 and 2), in accordance withone example embodiment of the present disclosure. Server system 301 mayalso include, but is not limited to, database server 116. In the exampleembodiment, server system 301 generates pest pressures prediction dataand heat map data as described herein.

Server system 301 includes a processor 305 for executing instructions.Instructions may be stored in a memory area 310, for example. Processor305 may include one or more processing units (e.g., in a multi-coreconfiguration) for executing instructions. The instructions may beexecuted within a variety of different operating systems on the serversystem 301, such as UNIX, LINUX, Microsoft Windows®, etc. It should alsobe appreciated that upon initiation of a computer-based method, variousinstructions may be executed during initialization. Some operations maybe required in order to perform one or more processes described herein,while other operations may be more general and/or specific to aparticular programming language (e.g., C, C#, C++, Java, or othersuitable programming languages, etc.).

Processor 305 is operatively coupled to a communication interface 315such that server system 301 is capable of communicating with a remotedevice such as a user system or another server system 301. For example,communication interface 315 may receive requests from a client system114 via the Internet, as illustrated in FIG. 2.

Processor 305 may also be operatively coupled to a storage device 134.Storage device 134 is any computer-operated hardware suitable forstoring and/or retrieving data. In some embodiments, storage device 134is integrated in server system 301. For example, server system 301 mayinclude one or more hard disk drives as storage device 134. In otherembodiments, storage device 134 is external to server system 301 and maybe accessed by a plurality of server systems 301. For example, storagedevice 134 may include multiple storage units such as hard disks orsolid state disks in a redundant array of inexpensive disks (RAID)configuration. Storage device 134 may include a storage area network(SAN) and/or a network attached storage (NAS) system.

In some embodiments, processor 305 is operatively coupled to storagedevice 134 via a storage interface 320. Storage interface 320 is anycomponent capable of providing processor 305 with access to storagedevice 134. Storage interface 320 may include, for example, an AdvancedTechnology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, aSmall Computer System Interface (SCSI) adapter, a RAID controller, a SANadapter, a network adapter, and/or any component providing processor 305with access to storage device 134.

Memory area 310 may include, but are not limited to, random accessmemory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-onlymemory (ROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), andnon-volatile RAM (NVRAM). The above memory types are examples only, andare thus not limiting as to the types of memory usable for storage of acomputer program.

FIG. 4 illustrates an example configuration of a client computing device402. Client computing device 402 may include, but is not limited to,client systems (“client computing devices”) 114. Client computing device402 includes a processor 404 for executing instructions. In someembodiments, executable instructions are stored in a memory area 406.Processor 404 may include one or more processing units (e.g., in amulti-core configuration). Memory area 406 is any device allowinginformation such as executable instructions and/or other data to bestored and retrieved. Memory area 406 may include one or morecomputer-readable media.

Client computing device 402 also includes at least one media outputcomponent 408 for presenting information to a user 400. Media outputcomponent 408 is any component capable of conveying information to user400. In some embodiments, media output component 408 includes an outputadapter such as a video adapter and/or an audio adapter. An outputadapter is operatively coupled to processor 404 and operativelycouplable to an output device such as a display device (e.g., a liquidcrystal display (LCD), organic light emitting diode (OLED) display,cathode ray tube (CRT), or “electronic ink” display) or an audio outputdevice (e.g., a speaker or headphones).

In some embodiments, client computing device 402 includes an inputdevice 410 for receiving input from user 400. Input device 410 mayinclude, for example, a keyboard, a pointing device, a mouse, a stylus,a touch sensitive panel (e.g., a touch pad or a touch screen), a camera,a gyroscope, an accelerometer, a position detector, and/or an audioinput device. A single component such as a touch screen may function asboth an output device of media output component 408 and input device410.

Client computing device 402 may also include a communication interface412, which is communicatively couplable to a remote device such asserver system 301 or a web server. Communication interface 412 mayinclude, for example, a wired or wireless network adapter or a wirelessdata transceiver for use with a mobile phone network (e.g., GlobalSystem for Mobile communications (GSM), 3G, 4G, 5G, or Bluetooth) orother mobile data network (e.g., Worldwide Interoperability forMicrowave Access (WIMAX)).

Stored in memory area 406 are, for example, computer-readableinstructions for providing a user interface to user 400 via media outputcomponent 408 and, optionally, receiving and processing input from inputdevice 410. A user interface may include, among other possibilities, aweb browser and client application. Web browsers enable users 400 todisplay and interact with media and other information typically embeddedon a web page or a website from a web server. A client applicationallows users 400 to interact with a server application. The userinterface, via one or both of a web browser and a client application,facilitates display of pest pressure information provided by PPPcomputing device 112. The client application may be capable of operatingin both an online mode (in which the client application is incommunication with PPP computing device 112) and an offline mode (inwhich the client application is not in communication with PPP computingdevice 112).

FIG. 5 is a flow diagram of an example method 500 for generating pestpressure data. Method 500 may be implemented, for example, using PPPcomputing device 112.

Method 500 includes receiving 502 trap data for a plurality of pesttraps in a geographic location. In the example embodiment, the trap dataincludes both current pest pressure and historical pest pressure at eachof the plurality of traps. The trap data may be received 502 from, forexample, trap data source 206 (shown in FIG. 2). Further, PPP computingdevice 112 may analyze the received 502 trap data to generate additionaldata. For example, from the received 502 trap data, PPP computing device112 may determine, for a number of different pest pressure levels (e.g.,defined by suitable upper and lower thresholds), the number of traps ateach level. Further, PPP computing device 112 may determine average pestpressures across a number of traps and/or across at least a portion ofthe geographic location. This additional data may be used in identifyingcorrelations and predicting future pest pressures, as described herein.

Method 500 further includes receiving 504 weather data 502 for thegeographic location. In the example embodiment, the weather dataincludes both current and historical weather conditions for thegeographic location. Further, in some embodiments, the weather data mayinclude predicted future weather conditions for the geographic location.The weather data may be received 504 from, for example, weather datasource 202 (shown in FIG. 2).

In the example embodiment, method 500 further includes receiving 506image data for the geographic location. The image data may include, forexample, satellite and/or drone image data. The image data may bereceived 506 from, for example, imaging data source 204 (shown in FIG.2).

Further, method 500 includes identifying 508 at least one geospatialfeature within the geographic location or proximate the geographiclocation.

As used herein, a ‘geospatial feature’ refers to a geographic feature orstructure that may have an impact on pest pressure. For example, ageographic feature may include a body of water (e.g., a river, a stream,a lake, etc.), an elevation feature (e.g., a mountain, a hill, a canyon,etc.), a transportation route (e.g., a road, a railroad track, etc.), afarm location, or a factory (e.g., a cotton factory).

In one embodiment, the at least geospatial feature is identified 508from existing map data. For example, PPP computing device 112 mayretrieve previously generated maps (e.g., topographical maps, elevationmaps, road maps, surveys, etc.) from a map data source (such as otherdata source 212 (shown in FIG. 2)), the previously generated mapsdemarcating the one or more geospatial features.

In another embodiment, PPP computing device 112 identifies 508 the oneor more geospatial features by analyzing the received 506 image data.For example, PPP computing device 112 may apply raster processing to theimage data to generate a digital elevation map, where each pixel (orother similar subdivision) of the digital elevation map is associatedwith an elevation value. Then, based on the elevation values, PPPcomputing device 112 identifies 508 the one or more geospatial featuresfrom the digital elevation map. For example, elevation features and/orbodies of water may be identified using such techniques.

Method 500 further includes applying 510 a machine learning algorithm tothe trap data, the weather data, the image data, and the at least oneidentified geospatial feature to identify a correlation between pestpressure and the at least one geospatial feature. Applying 510 themachine learning algorithm to the trap data, the weather data, the imagedata, and the at least one identified geospatial feature may be seen asapplying 510 a machine learning-based scheme to the trap data, theweather data, the image data, and the at least one identified geospatialfeature to identify a correlation between pest pressure and the at leastone geospatial feature. In one or more example embodiments, applying 510the machine learning algorithm to the trap data, the weather data, theimage data, and the at least one identified geospatial feature mayinclude determining a pest pressure value associated with a pest trap,based on a relation (e.g. a correlation) between pest pressure and theat least one geospatial feature.

In some embodiments, PPP computing device 112 may determine, by applying510 the machine learning algorithm, that pest pressure (e.g., at thelocation of a pest trap) varies based on a distance from the at leastone identified geospatial feature. For example, PPP computing device 112may determine that pest pressure is higher at locations proximate to abody of water (e.g., due to increased pest levels at the body of water).In another example, PPP computing device 112 may determine that pestpressure is higher at locations proximate a transportation route (e.g.,due to increased pest levels resulting from material transported alongthe transportation route). In yet another example, PPP computing device112 may determine that pest pressure is higher at locations proximate afactory (e.g., due to increased pest levels resulting from materialsprocessed at the factory). In yet other examples, PPP computing device112 may determine that pest pressure is reduced at locations proximatethe at least one identified geospatial feature.

Those of skill in the will appreciate that applying 510 the machinelearning algorithm may identify other correlations between pest pressureat the at least one geospatial feature. Specifically, the machinelearning algorithm considers the trap data, the weather data, the imagedata, and the at least one identified geospatial feature in combination,and is capable of detecting complex interactions between those differenttypes of data that may not be ascertainable by a human analyst. Forexample, non-distance-based correlations between the at least oneidentified geospatial feature and pest pressure may be identified insome embodiments.

For example, in one or more example embodiments, applying 510 themachine learning algorithm to the trap data, the weather data, the imagedata, and the at least one identified geospatial feature may includedetermining a pest pressure value associated with a pest trap, based ona model (e.g. a machine learning model, a pest lifecycle model)characterizing a relation (e.g. a correlation) between pest pressure andthe trap data (optionally wherein the trap data includes insect data,and/or developmental stage data of the insect). Further, in one or moreexample embodiments, applying 510 the machine learning algorithm to thetrap data, the weather data, the image data, and the at least oneidentified geospatial feature may include determining a pest pressurevalue associated with a pest trap, based on a model (e.g. a machinelearning model) characterizing a relation (e.g. a correlation) betweenpest pressure, the trap data, and the weather data.

Further, in some embodiments, pest pressure for a first pest may becorrelated to pest pressure for a second, different pest, and thatcorrelation may be detected using PPP computing device 112. For example,the at least one geospatial feature is a particular field having a knownhigh pest pressure for the second pest. Using the systems and methodsdescribed herein, PPP computing device 112 may determine that locationsproximate the particular field generally have a high pest pressure forthe first pest, which correlates to the pest pressure level of thesecond pest in the particular field. These “inter-pest” correlations maybe complex relationships that are identifiable by PPP computing device112, but that would not be identifiable by a human analyst. Similarly,“inter-crop” correlations may be identified by PPP computing device 112between nearby geographic locations that product different crops.

Subsequently, method 500 includes generating 512 predicted future pestpressures for the geographic location based on at least the identifiedcorrelation. Specifically, PPP computing device 112 uses the identifiedcorrelation, in combination with one or more models, algorithms, etc. topredict future pest pressure values for the geographic location. Forexample, PPP computing device 112 may utilize spray timer models, pestlifecycle models, etc. in combination with the identified correlation,trap data, weather data, and image data to generate 512 predicted futurepest pressures based on identified patterns. Those of skill in the artwill appreciate that other types of data may also be incorporated togenerated 512 predicted future pest pressures. For example, previouslyplanted crop data, neighboring farm data, field water level data, and/orsoil type data may be considered when predicting future pest pressures.

As one example of a model, developmental stages of a pest of interest(e.g., an insect, or a fungus) may be governed by an ambienttemperature. Accordingly, using a “degree day” model, developmentalstages of the pest may be predicted based on heat accumulation (e.g.,determined from temperature data).

The generated 512 predicted future pest pressures are one example ofpest pressure prediction data that may be transmitted to and displayedon a user computing device, such as client system 114 (shown in FIGS. 1and 2). For example, the predicted future pest pressures may betransmitted to the user computing device to cause the user computingdevice to present the predicted future pest pressures in a textual,graphical, and/or audio format, or any other suitable format. Asdescribed below in detail, in some embodiments, one or more heat mapsillustrating predicted future pest pressures are displayed on the usercomputing device.

From the generated 512 predicted future pest pressures, in someembodiments, the systems and methods described herein may also be usedto generate (e.g., using machine learning) a treatment recommendationfor the geographic location to address the predicted future pestpressures. For example, with an accurate prediction of future pestpressures in place, PPP computing device 112 may automatically generatea treatment plan for the geographic location to mitigate future levelsof high pest pressure. The treatment plan may specify, for example, oneor more substances (e.g., pesticides, fertilizers, etc.) and specifictimes when those one or more substances should be applied (e.g., daily,weekly etc.). Alternatively, the treatment plan may include other datato facilitate improving agricultural performance in view of predictedfuture pest pressures.

Further, in some embodiments, the generated 512 predicted future pestpressures are used (e.g., by PPP computing device 112) to controladditional systems. In one embodiment, a system for monitoring pestpressure (e.g., a system including pest traps) may be controlled basedon the predicted future pest pressures. For example, a reportingfrequency and/or type of trap data reported by one or more pest trapsmay be modified based on the predicted future pest pressures. In anotherexample, spraying equipment (e.g., for spraying pesticides) or otheragricultural equipment may be controlled based on the predicted futurepest pressures.

As noted above, PPP computing device 112 may also generate one or moreheat maps using pest pressure prediction data. For the purposes of thisdiscussion, PPP computing device 112 may be referred to herein as heatmap generation computing device 112.

FIG. 6 is a flow diagram of an example method 600 for generating heatmaps. Method 600 may be implemented, for example, using heat mapgeneration computing device 112 (shown in FIG. 1).

Method 600 includes receiving 602 trap data for a plurality of pesttraps in a geographic region. In the example embodiment, the trap dataincludes both current pest pressure and historical pest pressure at eachof the plurality of traps. The trap data may be received 602 from, forexample, trap data source 206 (shown in FIG. 2).

Further, method 600 includes receiving 604 weather data for thegeographic location. In the example embodiment, the weather dataincludes both current and historical weather conditions for thegeographic location. Further, in some embodiments, the weather data mayinclude predicted future weather conditions for the geographic location.The weather data may be received 604 from, for example, weather datasource 202 (shown in FIG. 2).

In the example embodiment, method 600 further includes receiving 606image data for the geographic location. The image data may include, forexample, satellite and/or drone image data. The image data may bereceived 606 from, for example, imaging data source 204 (shown in FIG.2).

Method 600 further includes applying 608 a machine learning algorithm tothe trap data, the weather data, and the image data to generatepredicted future pest pressure values at each of the plurality of pesttraps.

In addition, method 600 includes generating 610 a first heat map andgenerating 612 a second heat map. In the example embodiment, the firstheat map is associated with a first point in time and the second heatmap is associated with a difference, second point in time. The first andsecond heat maps may be generated 610, 612 as follows.

In the example embodiment, each heat map is generated by plotting aplurality of nodes on a map of the geographic location. Each nodecorresponds to the location of particular pest trap of the plurality ofpest traps. Further, in the example embodiment, each node is displayedin a color that represents the pest pressure value for the correspondingtest trap at the associated point in time. In one example, each node isdisplayed green (indicating a low pest pressure value), yellow(indicating a moderate pest pressure value), or red (indicating a highpest pressure value). In FIGS. 7-9, green is indicated by a diagonalline pattern, yellow is indicated by a cross hatch pattern, and red isindicated by a dot pattern. Those of skill in the art will appreciatethat other numbers of colors and different colors may be used in theembodiments described herein. Depending on the point in time associatedwith the heat map, the color of the node may indicate a past pestpressure value (if the point in time is in the past), a current pestpressure value (if the point in time is the present), or a predictedfuture pest pressure value (if the point in time is in the future). Thefuture predicted pest pressure values may be generated, for example,using machine learning algorithms, as described herein.

To complete the heat map, at least some of the remaining portions of themap including the colored nodes are colored. Specifically, remainingportions of the map are colored to generate a continuous map of pestpressure values. In the example embodiment, the remaining portions arecolored by interpolating between the pest pressure values at theplurality of nodes.

In one embodiment, interpolation is performed using an inverse distanceweighting (IDW) algorithm, wherein points on remaining portions of themap are colored based on their distance from known pest pressure valuesat the nodes. For example, in such an embodiment, pest pressure valuesfor locations without nodes may be calculated based on a weightedaverage of inverse distances nearby nodes. This embodiment operatesunder the assumption that pest pressure at a particular point will bemore strongly influenced by nodes that are closer (as opposed to moredistant nodes). In other embodiments, interpolation may be performedbased on other criteria in addition to, or alternative to distance fromthe nodes.

With pest pressure values generated for at least some of the remainingportions of the map (using interpolation, as described above), thoseportions are colored based on the generated pest pressure values. Aswith the nodes, in one example, green indicates a low pest pressurevalue, yellow indicates a moderate pest pressure value, and redindicates a high pest pressure value. The thresholds for the differentcolors may be set, for example, based on historical pest pressure, andmay be adjusted over time (automatically or based on user input). Thoseof skill in the art will appreciate that these three colors are onlyexamples, and that any suitable coloring scheme may be used to generatethe heat maps described herein.

In the example embodiment, the first and second heat maps are stored ina database, such as database 120 (shown in FIG. 1). Accordingly, in thisembodiment, when a user views heat maps on a user device (e.g., a mobilecomputing device), as described below, the heat maps have already beenpreviously generated and stored by heat map generation computing device112. Alternatively, heat maps may be generated and displayed inreal-time based on the user's request.

With the first and second heat maps generated 610, 612, in the exampleembodiment, method 600 further includes causing 614 a user interface todisplay a time lapse heat map. The user interface may be, for example, auser interface displayed on client device 114 (shown in FIGS. 1 and 2).The user interface may be implemented, for example, via an applicationinstalled on the client device 114 (e.g., an application provided by theentity that operates heat generation computing device 112).

The time lapse heat map displays an animation on the user interface.Specifically, in the example embodiment, the time lapse heat mapdynamically transitions between a plurality of previously generated heatmaps (e.g., the first and second heat maps) over time, as describedbelow. Accordingly, by viewing the dynamic heat map, users can easilysee and appreciate changes in pest pressure over time for the geographicregion. The time lapse heat map may display past, current, and/or futurepest pressure values for the geographic region.

It should be understood that, in example embodiment, the second heat mapfor a second point in time is generated using predicted pest pressurevalues, and this second point in time refers to a point in time laterthan the time of the most recent current and historical pest pressurevalues (e.g., included in the trap data) incorporated into the machinelearning algorithm. That is, the second point in time refers to a futurepoint in time in such embodiments.

With respect to the first heat map for a first point in time, in theexample embodiment, this is generated using pest pressure values for apoint in time earlier than the second point in time. Accordingly, thepest pressure values used for generating the first heat map aregenerally either current or historical pest pressure values. In anotherembodiment, the first point in time is also a future point in time, buta different point in time than the second point in time. Thus, the pestpressure values used for generating the first heat map are predictedpest pressure values as well.

Within the scope of this disclosure, it should be understood thatreference made herein to “a first heat map” and “a second heat map” andto “the first and second heat maps” can imply that one or more (e.g., aplurality of) “intermediate heat maps” are generated using pest pressurevalues (e.g. current, historical or predicted pest pressure values, asthe case may be) for various points in time between the first point intime and the second point in time. In such cases, the time lapse heatmap displays a dynamic transition between the first heat map, the one ormore intermediate heat maps, and the second heat map over time. In oneembodiment, the intermediate heat maps include one or more (e.g., aplurality of) intermediate heat maps generated using predicted pestpressure values. In another embodiment, the intermediate heat mapsinclude one or more (e.g., a plurality of) intermediate heat mapsgenerated using current and/or historical pest pressure values. In yetanother embodiment, the intermediate heat maps include one or more(e.g., a plurality of) intermediate heat maps generated using predictedpest pressure values and one or more (e.g., a plurality of) intermediateheat maps generated using current and/or historical pest pressurevalues.

In one embodiment, to display the time lapse heat map, each previouslygenerated heat map is displayed for a brief period of time beforeinstantaneously transitioning to the next heat map (e.g., in a slideshowformat). Alternatively, in some embodiments, heat map generationcomputing device 112 temporally interpolates between consecutive heatmaps to generate transition data (e.g., using machine learning) betweenthose heat maps. In such embodiments, the time lapse heat map displays asmooth evolution of pest pressure over time, instead of a series ofstatic images.

FIG. 7 is a first screenshot 700 of a user interface that may bedisplayed on a computing device, such as client system 114 (shown inFIGS. 1 and 2). The computing device may be, for example, a mobilecomputing device.

First screenshot 700 includes a pest pressure heat map 702 that displayspest pressure associated with a particular pest and crop in a region 704including a field 706. In the example shown in first screenshot 700, thepest is boll weevil and the crop is cotton. Those of skill in the artwill appreciate that the heat maps described herein may display pestpressure information for any suitable pest and crop. Further, in someembodiments, heat maps may display pest pressures for multiple pests inthe same crop, one pest in multiple crops, or multiple pests in multiplecrops.

As shown in FIG. 7, field 706 is demarcated on heat map 702 by a fieldboundary 708. Field boundary 708 may be plotted on heat map 702 by heatmap generation computing device 112 based on, for example, informationprovided by a grower associated with field 706. For example, the growermay provide information to heat map generation computing device 112 froma grower computing device, such as grower data source 210 (shown in FIG.2).

Heat map 702 includes three nodes 710, corresponding to three pest trapsin field 706. As shown in FIG. 7, each node 710 has an associated color(here two red nodes and one yellow node). Further, in heat map 702,locations not including nodes 710 are colored by interpolating the pestpressure values at nodes 710, generating a continuous map of pestpressure values. Although only three nodes 710 are shown in FIG. 7,those of skill in the art will appreciate that the additional pest trapsmay be used to color portions of heat map 702. In this example, heat map702 is a static heat map that shows pest pressure at a particular pointin time (e.g., one of the first and second heat maps described above).

First screenshot 700 further includes a time lapse button 712 that, whenselected by a user, causes a time lapse heat map to be displayed, asdescribed herein.

FIG. 8 is a second screenshot 800 of the user interface that may bedisplayed on a computing device, such as client system 114 (shown inFIGS. 1 and 2). Specifically, second screenshot 800 shows an enlargedview of heat map 702. The enlarged view may be generated, for example,in response to the user making a selection on the user interface tochange a zoom level.

As shown in FIG. 8, additional information not shown in first screenshot700 is shown in the enlarged view. For example, an additional node 802(representing an additional trap) is now visible. Further, an associatedtrap name is displayed with each node 710. In the exemplary embodiment,in the enlarged view, the user can select a particular node 710 to causethe user interface to display pest pressure data for that node 710. Thisis described in further detail below in association with FIG. 10.

FIG. 9 is a third screenshot 900 of the user interface that may bedisplayed on a computing device, such as client system 114 (shown inFIGS. 1 and 2). Specifically, third screenshot 900 shows a time lapseheat map 902. Time lapse heat map 902 may be displayed, for example, inresponse to the user selecting time lapse button 712 (shown in FIGS. 7and 8).

As shown in FIG. 9, a timeline 904 is displayed in association with timelapse heat map 902. Timeline 904 enables a user to quickly determinewhich time pest pressure is currently being shown for. Timeline 904shows a range of dates, including historical and future dates in theexample embodiment. Further, timeline 904 includes a current time marker906 indicating the current (i.e., present time), as well as a selectedtime marker 908 that indicates what time is associated with the pestpressure shown on time lapse heat map 902.

For example, in FIG. 9, timeline 904 extends from January 5 to February2, the current day is January 26, and time lapse heat map 902 shows pestpressures for January 29. Notably, the pest pressure shown in FIG. 9 isa predicted future pest pressure, as selected time marker 908 is laterthan current time marker 906.

In one embodiment, a user can adjust selected time marker 908 (e.g., byselecting and dragging selected time marker 908) to manipulate what timeis displayed by time lapse heat map 902. Further, in the exampleembodiment, when the user selects an activation icon 910, time lapseheat map 902 is displayed as an animation, automatically transitioningbetween different static heat maps to show the evolution of pestpressure over time. A stop icon 912 is also shown in screenshot 900.When the user has previously selected activation icon 910, the user canselect the stop icon 912 to stop the animation and freeze time lapseheat map 902 at a desired point in time.

FIG. 10 is a fourth screenshot 1000 of the user interface that may bedisplayed on a computing device, such as client system 114 (shown inFIGS. 1 and 2). Specifically, fourth screenshot 1000 shows pest pressuredata 1002 for a particular trap. Pest pressure data 1002 may bedisplayed, for example, in response to the user selecting a particularnode 710 (as described above in reference to FIG. 8). In one embodiment,pest pressure data 1002 includes graphical data 1004 that displays pestpressure over time (e.g., current and historical pest pressure) andtextual data 1006 that summarizes predicted future pest pressure.

Further, in some embodiments, the generated heat maps facilitatecontrolling additional systems. In one embodiment, a system formonitoring pest pressure (e.g., a system including pest traps) may becontrolled based on the heat maps. For example, a reporting frequencyand/or type of trap data reported by one or more pest traps may bemodified based on the heat maps. In another example, spraying equipment(e.g., for spraying pesticides) or other agricultural equipment may becontrolled based on the heat maps.

At least one of the technical problems addressed by this systemincludes: i) inability to accurately monitor pest pressure; ii)inability to accurately predict future pest pressure; and iii) inabilityto communicate pest pressure information to a user in a comprehensive,straightforward manner.

The technical effects provided by the embodiments described hereininclude at least i) monitoring pest pressure in real-time; ii)accurately predicting future pest pressure using machine learning; iii)controlling other systems or equipment based on predicted future pestpressures; iv) generating comprehensive heat maps illustrating pestpressure; v) generating time lapse heat maps that dynamically displaychanges in pest pressure over time; and vi) controlling other systems orequipment based on generated heat maps.

Further, a technical effect of the systems and processes describedherein is achieved by performing at least one of the following steps:(i) receiving trap data for a plurality of pest traps in a geographiclocation, the trap data including at least current and historical pestpressure at each of the plurality of pest traps; (ii) receiving weatherdata for the geographic location, the weather data including at leastcurrent and historical weather conditions for the geographic location;(iii) receiving image data for the geographic location; (iv) identifyingat least one geospatial feature within or proximate to the geographiclocation; (v) applying a machine learning algorithm to the trap data,the weather data, the image data, and the at least one identifiedgeospatial feature to identify a correlation between pest pressure andthe at least one geospatial feature; and (vi) generating predictedfuture pest pressures for the geographic location based at least on theidentified correlation between pest pressure and the at least onegeospatial feature.

A processor or a processing element in the embodiments described hereinmay employ artificial intelligence and/or be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data inorder to facilitate making predictions for subsequent data. Models maybe created based upon example inputs in order to make valid and reliablepredictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as image data, text data, report data, and/or numerical analysis.The machine learning programs may utilize deep learning algorithms thatmay be primarily focused on pattern recognition, and may be trainedafter processing multiple examples. The machine learning programs mayinclude Bayesian program learning (BPL), voice recognition andsynthesis, image or object recognition, optical character recognition,and/or natural language processing—either individually or incombination. The machine learning programs may also include naturallanguage processing, semantic analysis, automatic reasoning, and/ormachine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element may be required tofind its own structure in unlabeled example inputs. In one embodiment,machine learning techniques may be used to extract data about thecomputer device, the user of the computer device, the computer networkhosting the computer device, services executing on the computer device,and/or other data.

Based upon these analyses, the processing element may learn how toidentify characteristics and patterns that may then be applied toanalyzing trap data, weather data, image data, geospatial (e.g., usingone or more models) to predict future pest pressure.

As used herein, the term “non-transitory computer-readable media” isintended to be representative of any tangible computer-based deviceimplemented in any method or technology for short-term and long-termstorage of information, such as, computer-readable instructions, datastructures, program modules and sub-modules, or other data in anydevice. Therefore, the methods described herein may be encoded asexecutable instructions embodied in a tangible, non-transitory, computerreadable medium, including, without limitation, a storage device and/ora memory device. Such instructions, when executed by a processor, causethe processor to perform at least a portion of the methods describedherein. Moreover, as used herein, the term “non-transitorycomputer-readable media” includes all tangible, computer-readable media,including, without limitation, non-transitory computer storage devices,including, without limitation, volatile and nonvolatile media, andremovable and non-removable media such as a firmware, physical andvirtual storage, CD-ROMs, DVDs, and any other digital source such as anetwork or the Internet, as well as yet to be developed digital means,with the sole exception being a transitory, propagating signal.

This written description uses examples to disclose the disclosure,including the best mode, and also to enable any person skilled in theart to practice the embodiments, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

What is claimed is:
 1. A pest pressure prediction computing device comprising: a memory; and a processor communicatively coupled to the memory, the processor programmed to: receive trap data for a plurality of pest traps in a geographic location, the trap data including at least current and historical pest pressure at each of the plurality of pest traps; receive weather data for the geographic location, the weather data including at least current and historical weather conditions for the geographic location; receive image data for the geographic location; identify at least one geospatial feature within or proximate to the geographic location; apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature; and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.
 2. The pest pressure prediction computing device of claim 1, wherein the at least one geospatial feature includes at least one of an elevation feature, a body of water, a transportation route, and a factory.
 3. The pest pressure prediction computing device as in any of claims 1-2, wherein the processor is further programmed to: generate a treatment recommendation for the geographic location based on the predicted future pest pressures.
 4. The pest pressure prediction computing device as in any of claims 1-3, wherein the processor is further programmed to: transmit the predicted pest future pest pressures to a mobile computing device to cause the predicted pest pressures to be displayed on the mobile computing device, the predicted pest pressures displayed via an application installed on the mobile computing device.
 5. The pest pressure prediction computing device as in any of claims 1-4, wherein the weather data includes predicted future weather conditions for the geographic location.
 6. The pest pressure prediction computing device as in any of claims 1-5, wherein the identified correlation is a correlation between pest pressure and at least a distance from the at least one geospatial feature.
 7. The pest pressure prediction computing device as in any of claims 1-6, wherein the processor is further programmed to control at least one of a pest monitoring system and agricultural equipment based on the future predicted pest pressures.
 8. A method for generating pest pressure predication data, the method implemented using a pest pressure prediction computing device including a memory communicatively coupled to a processor, the method comprising: receiving trap data for a plurality of pest traps in a geographic location, the trap data including at least current and historical pest pressure at each of the plurality of pest traps; receiving weather data for the geographic location, the weather data including at least current and historical weather conditions for the geographic location; receiving image data for the geographic location; identifying at least one geospatial feature within or proximate to the geographic location; applying a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature; and generating predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.
 9. The method of claim 8, wherein the at least one geospatial feature includes at least one of an elevation feature, a body of water, a transportation route, and a factory.
 10. The method as in any of claims 8-9, further comprising: generating a treatment recommendation for the geographic location based on the predicted future pest pressures.
 11. The method as in any of claims 8-10, further comprising: transmitting the predicted pest future pest pressures to a mobile computing device to cause the predicted pest pressures to be displayed on the mobile computing device, the predicted pest pressures displayed via an application installed on the mobile computing device.
 12. The method as in any of claims 8-11, wherein the weather data includes predicted future weather conditions for the geographic location.
 13. The method as in any of claims 8-12, wherein the identified correlation is a correlation between pest pressure and at least a distance from the at least one geospatial feature.
 14. The method as in any of claims 8-13, further comprising controlling at least one of a pest monitoring system and agricultural equipment based on the future predicted pest pressures.
 15. A computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by a pest pressure prediction computing device including at least one processor in communication with a memory, the computer-readable instructions cause the pest pressure prediction computing device to: receive trap data for a plurality of pest traps in a geographic location, the trap data including at least current and historical pest pressure at each of the plurality of pest traps; receive weather data for the geographic location, the weather data including at least current and historical weather conditions for the geographic location; receive image data for the geographic location; identify at least one geospatial feature within or proximate to the geographic location; apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature; and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.
 16. The computer-readable storage medium of claim 15, wherein the at least one geospatial feature includes at least one of an elevation feature, a body of water, a transportation route, and a factory.
 17. The computer-readable storage medium as in any of claims 15-16, wherein the instructions further cause the pest pressure prediction computing device to: generate a treatment recommendation for the geographic location based on the predicted future pest pressures.
 18. The computer-readable storage medium as in any of claims 15-17, wherein the instructions further cause the pest pressure prediction computing device to: transmit the predicted pest future pest pressures to a mobile computing device to cause the predicted pest pressures to be displayed on the mobile computing device, the predicted pest pressures displayed via an application installed on the mobile computing device.
 19. The computer-readable storage medium as in any of claims 15-18, wherein the weather data includes predicted future weather conditions for the geographic location.
 20. The computer-readable storage medium as in any of claims 15-19, wherein the identified correlation is a correlation between pest pressure and at least a distance from the at least one geospatial feature. 